Brain-wide and cell-specific transcriptomic insights into MRI-derived cortical morphology in macaque monkeys

Integrative analyses of transcriptomic and neuroimaging data have generated a wealth of information about biological pathways underlying regional variability in imaging-derived brain phenotypes in humans, but rarely in nonhuman primates due to the lack of a comprehensive anatomically-defined atlas of brain transcriptomics. Here we generate complementary bulk RNA-sequencing dataset of 819 samples from 110 brain regions and single-nucleus RNA-sequencing dataset, and neuroimaging data from 162 cynomolgus macaques, to examine the link between brain-wide gene expression and regional variation in morphometry. We not only observe global/regional expression profiles of macaque brain comparable to human but unravel a dorsolateral-ventromedial gradient of gene assemblies within the primate frontal lobe. Furthermore, we identify a set of 971 protein-coding and 34 non-coding genes consistently associated with cortical thickness, specially enriched for neurons and oligodendrocytes. These data provide a unique resource to investigate nonhuman primate models of human diseases and probe cross-species evolutionary mechanisms.


March 2021
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For bulk RNA-seq analysis, samples with low quality (% unique mapping < 50%, Z > 2 for %Intergenic Bases, GC Dropout rate, or AT Dropout rate and Z < ! 2 for %High-quality Aligned Reads, %mRNA Bases, or Median 5# to 3# Bias) were identified as outliers, and any sample with greater than two outlier indexes was removed. Totally, 59 samples were removed. For snRNA-seq analysis, we excluded nuclei based on quality control procedure and nuclei with detected genes less than 500 and more than 7500 were removed for further analysis. One subject at age 9 was excluded for MRI data analysis as only one subject at that age.
Two snRNA-seq data in macaques were analyzed to ensure reproducibility. Cell type and functional enrichments for WGCNA modules and 1,005 CT-related genes were replicated with these two snRNA-seq data. AUCell and marker genes sensitivity analyses were also applied. Different combinations of minimum count and minimum number of samples were test for evaluation the expressed genes and CT-related genes retained.
All group assignments were pre-determined based on known age. To robustly estimate p values in our neuroimagingtranscriptomic analyses, we employ multiple randomization (permutation) strategies. To examine the effect of sex on our results, we replicated the above analyses using male-only imaging and transcriptome data. See Methods for additional details on these procedures.
Blinding was not relevant for the study. All group assignments were known based on known ages. Distribution of individuals in MRI data by age and sex was provided ( Supplementary Fig. 17).
For transcriptomics data, ages of each adult monkeys (Macaca fascicularis) were provided in Supplementary Data 1. For MRI data, 161 Macaca fascicularis ranging between 2 and 8 years (only one subject at age 9 was excluded hence) was included. Distribution of individuals by age was provided in Supplementary Fig. 17.

This study did not involve wild animals
For transcriptomics data, 8 male and 1 female monkeys were included. Similar expression pattern was observed between male and feamle macaques (r=0.964) and PCA analysis revealed that sex is not the major source of variation. For MRI data, 72 female subjects and 89 male subjects were involved. The effect of the sex on the PLS analysis was also evaluated using linear modelling and male-only imaging and transcription data.
This study did not involve samples collected from the field.
All animal experimental procedures were approved by the Animal Care and Use Committee of CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences.

March 2021
Magnetic resonance imaging Five to 7 sets of whole-brain images for each animal. Detailed information was provided in the published paper (doi: 10.1093/cercor/bhaa229).
Only structural MRI was used in the present study and no behavioral performance was measured.

Structural 3 Tesla
High-resolution T1-weighted anatomical images of macaque brain were acquired with key parameters as follows: TR = 2300 ms; TE = 3 ms; inversion time = 1000 ms; flip angle = 9$; acquisition voxel size = 0.5 × 0.5 × 0.5 mm3. Five to 7 whole-brain anatomical volumes were recorded for each subject. -Cortical thickness estimated using the diffeomorphic registration-based cortical thickness (DiReCT) method implemented in ANTs Individual brain was normalized to a brain atlas of cynomolgus macaques using ANTs. Detailed information was provided in the published paper (doi: 10.1093/cercor/bhaa229).
Data was not volume censored.
For age trajectory of normative structural variation, Gaussian process regression was developed and used age as a covariate to predict brain measures.Detailed information was available in the published paper (doi: 10.1093/cercor/bhaa229). For imaging-transcriptomics analysis, multivariate methods (PLS regression) was performed. The statistical significance of the PLS explained variance was evaluated by permuting the response variables 10000 times.
See previous point.
Whole-brain parcellation continuous values were carried forward to neuroimaging-transcriptomic analyses.
See Methods and above for descriptions on the multiple permutation strategies employed to test robustness of empirical effects.